Overview

Dataset statistics

Number of variables12
Number of observations153
Missing cells2
Missing cells (%)0.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory13.2 KiB
Average record size in memory88.4 B

Variable types

NUM10
CAT2

Warnings

Happiness Score is highly correlated with Happiness RankHigh correlation
Happiness Rank is highly correlated with Happiness ScoreHigh correlation
Job Satisfaction has 2 (1.3%) missing values Missing
Country has unique values Unique
Happiness Rank has unique values Unique
Economy has unique values Unique
Family has unique values Unique
Health has unique values Unique
Freedom has unique values Unique
Generosity has unique values Unique
Corruption has unique values Unique
Dystopia has unique values Unique

Reproduction

Analysis started2020-09-15 15:11:38.010175
Analysis finished2020-09-15 15:12:43.934013
Duration1 minute and 5.92 seconds
Software versionpandas-profiling v2.9.0
Download configurationconfig.yaml

Variables

Country
Categorical

UNIQUE

Distinct153
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size612.0 B
Iceland
 
1
Chad
 
1
Tunisia
 
1
Kosovo
 
1
Panama
 
1
Other values (148)
148 
ValueCountFrequency (%) 
Iceland10.7%
 
Chad10.7%
 
Tunisia10.7%
 
Kosovo10.7%
 
Panama10.7%
 
Finland10.7%
 
Norway10.7%
 
Slovenia10.7%
 
Israel10.7%
 
Bulgaria10.7%
 
Other values (143)14393.5%
 
2020-09-15T20:42:44.490810image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Frequencies of value counts

Unique

Unique153 ?
Unique (%)100.0%
2020-09-15T20:42:44.996296image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length24
Median length7
Mean length8.300653595
Min length4

Happiness Rank
Real number (ℝ≥0)

HIGH CORRELATION
UNIQUE

Distinct153
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean78.16993464
Minimum1
Maximum155
Zeros0
Zeros (%)0.0%
Memory size1.2 KiB
2020-09-15T20:42:45.573042image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile8.6
Q140
median78
Q3117
95-th percentile147.4
Maximum155
Range154
Interquartile range (IQR)77

Descriptive statistics

Standard deviation45.0087405
Coefficient of variation (CV)0.5757807105
Kurtosis-1.202442518
Mean78.16993464
Median Absolute Deviation (MAD)39
Skewness-0.005316117057
Sum11960
Variance2025.786722
MonotocityStrictly increasing
2020-09-15T20:42:46.088918image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
15510.7%
 
4910.7%
 
5610.7%
 
5510.7%
 
5410.7%
 
5310.7%
 
5210.7%
 
5110.7%
 
5010.7%
 
4810.7%
 
Other values (143)14393.5%
 
ValueCountFrequency (%) 
110.7%
 
210.7%
 
310.7%
 
410.7%
 
510.7%
 
ValueCountFrequency (%) 
15510.7%
 
15410.7%
 
15310.7%
 
15210.7%
 
15110.7%
 

Happiness Score
Real number (ℝ≥0)

HIGH CORRELATION

Distinct149
Distinct (%)97.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.349281046
Minimum2.693000078
Maximum7.537000179
Zeros0
Zeros (%)0.0%
Memory size1.2 KiB
2020-09-15T20:42:46.718946image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum2.693000078
5-th percentile3.567800045
Q14.497000217
median5.278999805
Q36.09800005
95-th percentile7.296000004
Maximum7.537000179
Range4.844000101
Interquartile range (IQR)1.600999833

Descriptive statistics

Standard deviation1.134997316
Coefficient of variation (CV)0.2121775442
Kurtosis-0.755791976
Mean5.349281046
Median Absolute Deviation (MAD)0.813999652
Skewness0.01690561187
Sum818.4400001
Variance1.288218906
MonotocityDecreasing
2020-09-15T20:42:47.244330image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
6.45399999621.3%
 
5.07399988221.3%
 
7.2839999221.3%
 
5.83799982121.3%
 
4.69500017210.7%
 
3.47099995610.7%
 
5.33599996610.7%
 
3.79500007610.7%
 
4.60799980210.7%
 
4.64400005310.7%
 
Other values (139)13990.8%
 
ValueCountFrequency (%) 
2.69300007810.7%
 
2.90499997110.7%
 
3.34899997710.7%
 
3.46199989310.7%
 
3.47099995610.7%
 
ValueCountFrequency (%) 
7.53700017910.7%
 
7.52199983610.7%
 
7.50400018710.7%
 
7.49399995810.7%
 
7.46899986310.7%
 

Economy
Real number (ℝ≥0)

UNIQUE

Distinct153
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.982433192
Minimum0
Maximum1.870765686
Zeros1
Zeros (%)0.7%
Memory size1.2 KiB
2020-09-15T20:42:47.993212image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.240452218
Q10.659516692
median1.064577937
Q31.315175295
95-th percentile1.548425508
Maximum1.870765686
Range1.870765686
Interquartile range (IQR)0.655658603

Descriptive statistics

Standard deviation0.4219009555
Coefficient of variation (CV)0.4294449322
Kurtosis-0.6877592579
Mean0.982433192
Median Absolute Deviation (MAD)0.29136014
Skewness-0.3842690838
Sum150.3122784
Variance0.1780004163
MonotocityNot monotonic
2020-09-15T20:42:48.643200image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
0.98240941810.7%
 
1.48709726310.7%
 
1.18529546310.7%
 
1.15318381810.7%
 
1.48792338410.7%
 
0.09210234910.7%
 
0.36842092910.7%
 
1.15360176610.7%
 
1.28177809710.7%
 
1.50394463510.7%
 
Other values (143)14393.5%
 
ValueCountFrequency (%) 
010.7%
 
0.02264318410.7%
 
0.09162256910.7%
 
0.09210234910.7%
 
0.11904179310.7%
 
ValueCountFrequency (%) 
1.87076568610.7%
 
1.74194359810.7%
 
1.6922776710.7%
 
1.63295245210.7%
 
1.62634336910.7%
 

Family
Real number (ℝ≥0)

UNIQUE

Distinct153
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.186630454
Minimum0
Maximum1.610574007
Zeros1
Zeros (%)0.7%
Memory size1.2 KiB
2020-09-15T20:42:49.247966image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.6184054016
Q11.041989803
median1.251825571
Q31.416403651
95-th percentile1.522983408
Maximum1.610574007
Range1.610574007
Interquartile range (IQR)0.374413848

Descriptive statistics

Standard deviation0.288441075
Coefficient of variation (CV)0.2430757394
Kurtosis1.481827291
Mean1.186630454
Median Absolute Deviation (MAD)0.179480434
Skewness-1.16138699
Sum181.5544595
Variance0.08319825376
MonotocityNot monotonic
2020-09-15T20:42:49.866419image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
1.45994496310.7%
 
1.25558519410.7%
 
1.13136327310.7%
 
1.28667759910.7%
 
0.60132312810.7%
 
1.38478863210.7%
 
1.27749133110.7%
 
1.36704301810.7%
 
1.55112159310.7%
 
1.43388521710.7%
 
Other values (143)14393.5%
 
ValueCountFrequency (%) 
010.7%
 
0.39610260710.7%
 
0.4318825310.7%
 
0.43529984410.7%
 
0.51256883110.7%
 
ValueCountFrequency (%) 
1.61057400710.7%
 
1.55823111510.7%
 
1.55112159310.7%
 
1.5489691510.7%
 
1.54819512410.7%
 

Health
Real number (ℝ≥0)

UNIQUE

Distinct153
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.5501173712
Minimum0
Maximum0.949492395
Zeros1
Zeros (%)0.7%
Memory size1.2 KiB
2020-09-15T20:42:50.572330image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.1110552562
Q10.364509284
median0.606041551
Q30.719216824
95-th percentile0.8448646186
Maximum0.949492395
Range0.949492395
Interquartile range (IQR)0.35470754

Descriptive statistics

Standard deviation0.2377685883
Coefficient of variation (CV)0.4322142887
Kurtosis-0.6014299639
Mean0.5501173712
Median Absolute Deviation (MAD)0.168245077
Skewness-0.5719030585
Sum84.16795779
Variance0.05653390157
MonotocityNot monotonic
2020-09-15T20:42:51.164580image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
0.68776345310.7%
 
0.72679823610.7%
 
0.19676375410.7%
 
0.83087515810.7%
 
0.58946520110.7%
 
0.63760560810.7%
 
0.40936285310.7%
 
0.90021407610.7%
 
0.80533593910.7%
 
0.16348600410.7%
 
Other values (143)14393.5%
 
ValueCountFrequency (%) 
010.7%
 
0.00556475410.7%
 
0.01877268610.7%
 
0.04113471510.7%
 
0.0486421710.7%
 
ValueCountFrequency (%) 
0.94949239510.7%
 
0.94306242510.7%
 
0.91347587110.7%
 
0.90021407610.7%
 
0.888960610.7%
 

Freedom
Real number (ℝ≥0)

UNIQUE

Distinct153
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.4084890296
Minimum0
Maximum0.658248663
Zeros1
Zeros (%)0.7%
Memory size1.2 KiB
2020-09-15T20:42:51.761195image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.1162566736
Q10.3007406
median0.437454283
Q30.518630743
95-th percentile0.6133793114
Maximum0.658248663
Range0.658248663
Interquartile range (IQR)0.217890143

Descriptive statistics

Standard deviation0.150744203
Coefficient of variation (CV)0.3690287672
Kurtosis-0.2330639484
Mean0.4084890296
Median Absolute Deviation (MAD)0.111746371
Skewness-0.6101040659
Sum62.49882153
Variance0.02272381474
MonotocityNot monotonic
2020-09-15T20:42:52.327655image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
0.37792226710.7%
 
0.45494338910.7%
 
0.44975057210.7%
 
0.14706243610.7%
 
0.60413098310.7%
 
0.58521467410.7%
 
0.25645071310.7%
 
0.28211015510.7%
 
0.33288118210.7%
 
0.54750937210.7%
 
Other values (143)14393.5%
 
ValueCountFrequency (%) 
010.7%
 
0.01499585510.7%
 
0.03036985710.7%
 
0.05990075310.7%
 
0.08153944510.7%
 
ValueCountFrequency (%) 
0.65824866310.7%
 
0.63542258710.7%
 
0.63337582310.7%
 
0.62716263510.7%
 
0.62600672210.7%
 

Generosity
Real number (ℝ≥0)

UNIQUE

Distinct153
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.2453236803
Minimum0
Maximum0.838075161
Zeros1
Zeros (%)0.7%
Memory size1.2 KiB
2020-09-15T20:42:52.898668image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.051235636
Q10.153074786
median0.231503338
Q30.322228104
95-th percentile0.4898676934
Maximum0.838075161
Range0.838075161
Interquartile range (IQR)0.169153318

Descriptive statistics

Standard deviation0.1343946971
Coefficient of variation (CV)0.547826027
Kurtosis1.85164283
Mean0.2453236803
Median Absolute Deviation (MAD)0.083887324
Skewness0.9216401934
Sum37.53452308
Variance0.0180619346
MonotocityNot monotonic
2020-09-15T20:42:53.516907image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
0.13351991810.7%
 
0.39409616610.7%
 
0.32666242110.7%
 
0.15011246510.7%
 
0.23150333810.7%
 
0.42858037410.7%
 
0.22415065810.7%
 
0.49086356210.7%
 
0.44486030910.7%
 
0.42785832310.7%
 
Other values (143)14393.5%
 
ValueCountFrequency (%) 
010.7%
 
0.01016465710.7%
 
0.02880684110.7%
 
0.03220995510.7%
 
0.04378537810.7%
 
ValueCountFrequency (%) 
0.83807516110.7%
 
0.61170458810.7%
 
0.57473057510.7%
 
0.5721231110.7%
 
0.50000512610.7%
 

Corruption
Real number (ℝ≥0)

UNIQUE

Distinct153
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.1231792456
Minimum0
Maximum0.464307785
Zeros1
Zeros (%)0.7%
Memory size1.2 KiB
2020-09-15T20:42:54.024590image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.0200243458
Q10.057069719
median0.08984752
Q30.153066069
95-th percentile0.3414966524
Maximum0.464307785
Range0.464307785
Interquartile range (IQR)0.09599635

Descriptive statistics

Standard deviation0.1021328274
Coefficient of variation (CV)0.8291399001
Kurtosis1.634935377
Mean0.1231792456
Median Absolute Deviation (MAD)0.043178778
Skewness1.474141202
Sum18.84642457
Variance0.01043111442
MonotocityNot monotonic
2020-09-15T20:42:54.627499image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
0.05930780610.7%
 
0.09566501510.7%
 
0.0782135510.7%
 
0.1190946410.7%
 
0.08928260210.7%
 
0.06024135610.7%
 
0.29393374910.7%
 
0.13563878810.7%
 
0.0926102110.7%
 
0.09222688510.7%
 
Other values (143)14393.5%
 
ValueCountFrequency (%) 
010.7%
 
0.00438790110.7%
 
0.00896481610.7%
 
0.01009128610.7%
 
0.01105153110.7%
 
ValueCountFrequency (%) 
0.46430778510.7%
 
0.45522001410.7%
 
0.43929925610.7%
 
0.40077006810.7%
 
0.38439872910.7%
 

Dystopia
Real number (ℝ≥0)

UNIQUE

Distinct153
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.85307234
Minimum0.377913713
Maximum3.11748457
Zeros0
Zeros (%)0.0%
Memory size1.2 KiB
2020-09-15T20:42:55.180698image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0.377913713
5-th percentile1.05412054
Q11.597970247
median1.832909822
Q32.150801182
95-th percentile2.736463165
Maximum3.11748457
Range2.739570857
Interquartile range (IQR)0.552830935

Descriptive statistics

Standard deviation0.499490415
Coefficient of variation (CV)0.2695471753
Kurtosis0.7339869208
Mean1.85307234
Median Absolute Deviation (MAD)0.276046991
Skewness-0.2425282526
Sum283.520068
Variance0.2494906747
MonotocityNot monotonic
2020-09-15T20:42:55.705055image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
1.65190219910.7%
 
1.3194651610.7%
 
0.37791371310.7%
 
1.55231189710.7%
 
3.1174845710.7%
 
1.99365520510.7%
 
2.80175733610.7%
 
2.22495865810.7%
 
1.69716763510.7%
 
1.78964614910.7%
 
Other values (143)14393.5%
 
ValueCountFrequency (%) 
0.37791371310.7%
 
0.41938924810.7%
 
0.54006123510.7%
 
0.55463314110.7%
 
0.62113046610.7%
 
ValueCountFrequency (%) 
3.1174845710.7%
 
2.89863920210.7%
 
2.89389109610.7%
 
2.83715486510.7%
 
2.80780839910.7%
 

Job Satisfaction
Real number (ℝ≥0)

MISSING

Distinct126
Distinct (%)83.4%
Missing2
Missing (%)1.3%
Infinite0
Infinite (%)0.0%
Mean75.20993377
Minimum44.4
Maximum95.1
Zeros0
Zeros (%)0.0%
Memory size1.2 KiB
2020-09-15T20:42:56.394216image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum44.4
5-th percentile51.35
Q168.95
median78.1
Q385.1
95-th percentile93.05
Maximum95.1
Range50.7
Interquartile range (IQR)16.15

Descriptive statistics

Standard deviation12.96236478
Coefficient of variation (CV)0.1723491051
Kurtosis-0.5492301348
Mean75.20993377
Median Absolute Deviation (MAD)7.8
Skewness-0.6138585643
Sum11356.7
Variance168.0229007
MonotocityNot monotonic
2020-09-15T20:42:56.969166image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
79.853.3%
 
82.132.0%
 
71.132.0%
 
73.721.3%
 
82.421.3%
 
8021.3%
 
74.921.3%
 
7421.3%
 
73.321.3%
 
81.421.3%
 
Other values (116)12682.4%
 
ValueCountFrequency (%) 
44.410.7%
 
44.810.7%
 
45.310.7%
 
48.510.7%
 
49.310.7%
 
ValueCountFrequency (%) 
95.110.7%
 
94.610.7%
 
94.510.7%
 
93.810.7%
 
93.721.3%
 

Region
Categorical

Distinct7
Distinct (%)4.6%
Missing0
Missing (%)0.0%
Memory size612.0 B
Africa
44 
Asia-Pacific
43 
Latin America
22 
Eastern Europe
22 
Western Europe
19 
Other values (2)
 
3
ValueCountFrequency (%) 
Africa4428.8%
 
Asia-Pacific4328.1%
 
Latin America2214.4%
 
Eastern Europe2214.4%
 
Western Europe1912.4%
 
North America21.3%
 
Europe10.7%
 
2020-09-15T20:42:57.676446image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Frequencies of value counts

Unique

Unique1 ?
Unique (%)0.7%
2020-09-15T20:42:58.036475image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-15T20:42:58.536632image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length14
Median length12
Mean length10.92810458
Min length6

Interactions

2020-09-15T20:41:56.119418image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-15T20:41:57.230031image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-15T20:41:57.566834image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-15T20:41:57.866936image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-15T20:41:58.273547image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-15T20:41:58.716927image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-15T20:41:59.117680image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-15T20:41:59.551252image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-15T20:41:59.899591image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-15T20:42:00.276050image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-15T20:42:00.579955image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-15T20:42:00.869359image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-15T20:42:01.145990image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-15T20:42:01.433825image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-15T20:42:01.718159image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-15T20:42:02.143415image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-15T20:42:03.754900image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-15T20:42:04.026092image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-15T20:42:04.320473image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-15T20:42:04.601929image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-15T20:42:04.961423image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-15T20:42:05.288297image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-15T20:42:05.617417image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-15T20:42:05.927302image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-15T20:42:06.234516image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-15T20:42:06.545501image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-15T20:42:06.855767image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-15T20:42:07.231482image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-15T20:42:07.675961image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-15T20:42:08.102104image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-15T20:42:08.535897image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-15T20:42:09.024163image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-15T20:42:09.499181image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-15T20:42:09.913361image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-15T20:42:10.347395image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-15T20:42:10.742167image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-15T20:42:11.194918image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-15T20:42:11.744304image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-15T20:42:12.313769image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-15T20:42:12.747538image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-15T20:42:13.170524image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-15T20:42:13.578082image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-15T20:42:14.433992image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-15T20:42:14.835604image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-15T20:42:15.456817image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-15T20:42:15.967035image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-15T20:42:16.514156image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-15T20:42:16.957807image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-15T20:42:17.343320image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-15T20:42:17.923454image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-15T20:42:18.384388image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-15T20:42:18.796511image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-15T20:42:19.248572image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-15T20:42:19.663877image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-15T20:42:20.151466image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-15T20:42:20.538471image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-15T20:42:21.148185image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-15T20:42:21.569556image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-15T20:42:21.979735image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-15T20:42:22.381885image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-15T20:42:22.763945image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-15T20:42:23.149372image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-15T20:42:23.530067image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-15T20:42:23.918053image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-15T20:42:24.317777image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-15T20:42:24.670041image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-15T20:42:25.046823image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-15T20:42:25.464667image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-15T20:42:25.887085image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-15T20:42:26.302055image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-15T20:42:26.879915image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-15T20:42:27.314455image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-15T20:42:27.703434image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-15T20:42:28.092172image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-15T20:42:28.496291image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-15T20:42:28.918845image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-15T20:42:29.540115image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-15T20:42:29.918461image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-15T20:42:30.404196image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-15T20:42:30.941595image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-15T20:42:31.582156image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-15T20:42:32.097544image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-15T20:42:32.525482image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-15T20:42:32.978936image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-15T20:42:33.424272image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-15T20:42:33.870728image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-15T20:42:34.262939image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-15T20:42:34.632795image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-15T20:42:35.050400image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-15T20:42:35.430164image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-15T20:42:35.818901image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-15T20:42:36.199187image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-15T20:42:36.572592image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-15T20:42:36.950319image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-15T20:42:37.332966image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-15T20:42:37.681640image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-15T20:42:38.057780image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-15T20:42:38.398253image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-15T20:42:38.761712image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-15T20:42:39.316883image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Correlations

2020-09-15T20:42:59.077569image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2020-09-15T20:43:00.061275image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2020-09-15T20:43:00.729050image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2020-09-15T20:43:01.344780image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2020-09-15T20:42:40.118809image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-15T20:42:40.828971image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-15T20:42:43.624231image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Sample

First rows

CountryHappiness RankHappiness ScoreEconomyFamilyHealthFreedomGenerosityCorruptionDystopiaJob SatisfactionRegion
0Norway17.5371.6164631.5335240.7966670.6354230.3620120.3159642.27702794.6Western Europe
1Denmark27.5221.4823831.5511220.7925660.6260070.3552800.4007702.31370793.5Western Europe
2Iceland37.5041.4806331.6105740.8335520.6271630.4755400.1535272.32271594.5Western Europe
3Switzerland47.4941.5649801.5169120.8581310.6200710.2905490.3670072.27671693.7Western Europe
4Finland57.4691.4435721.5402470.8091580.6179510.2454830.3826122.43018291.2Western Europe
5Netherlands67.3771.5039451.4289390.8106960.5853840.4704900.2826622.29480493.8Western Europe
6Canada77.3161.4792041.4813490.8345580.6111010.4355400.2873722.18726490.5North America
7New Zealand87.3141.4057061.5481950.8167600.6140620.5000050.3828172.04645688.6Asia-Pacific
8Sweden97.2841.4943871.4781620.8308750.6129240.3853990.3843992.09753892.7Western Europe
9Australia107.2841.4844151.5100420.8438870.6016070.4776990.3011842.06521189.2Asia-Pacific

Last rows

CountryHappiness RankHappiness ScoreEconomyFamilyHealthFreedomGenerosityCorruptionDystopiaJob SatisfactionRegion
143Yemen1463.5930.5916830.9353820.3100810.2494640.1041250.0567671.34560158.9Asia-Pacific
144South Sudan1473.5910.3972490.6013230.1634860.1470620.2856710.1167941.879567NaNAfrica
145Liberia1483.5330.1190420.8721180.2299180.3328810.2665500.0389481.67328656.6Africa
146Guinea1493.5070.2445500.7912450.1941290.3485880.2648150.1109381.55231255.1Africa
147Togo1503.4950.3054450.4318830.2471060.3804260.1968960.0956651.83722944.8Africa
148Rwanda1513.4710.3687460.9457070.3264250.5818440.2527560.4552200.54006151.7Africa
149Syria1523.4620.7771530.3961030.5005330.0815390.4936640.1513471.06157462.7Asia-Pacific
150Tanzania1533.3490.5111361.0419900.3645090.3900180.3542560.0660350.62113057.8Africa
151Burundi1542.9050.0916230.6297940.1516110.0599010.2044350.0841481.68302454.3Africa
152Central African Republic1552.6930.0000000.0000000.0187730.2708420.2808760.0565652.06600570.4Africa